Nathalie Risso
Nathalie Risso is a tenure-track assistant professor with the School of Mining Engineering and Mineral Resources at the University of Arizona. Risso is the director of the Mine Automation and Autonomous Systems Laboratory, which focuses on the integration of automation under a cyber-physical systems approach to enable autonomous behavior in safety-critical environments, such as those in mining applications. Her research has a strong focus on the development of solutions for mining applications intended to operate in harsh and low connectivity environments, where safety, robustness, and autonomous systems collaboration are design requirements. Risso is one of the recipients of the 2023 SME Freeport-McMoRan, Inc. Career Development Grant to develop research related to AI-driven cyber-physical systems for mining applications. She has extensive experience as a consultant in the development of automation and autonomous systems solutions for the mining and energy industry sectors. Risso has led several R+D industry and government initiatives in the area of AI, machine learning and advanced control systems.
Degrees
- PhD Electrical and Computer Engineering
- University of Arizona, United States
- MS Electrical and Computer Engineering
- University of Arizona, United States
- BS Electrical Engineering
- Universidad de Concepcion, Concepcion, Chile
Work Experience
- Engineering Consultant, Freelance, (2017-Ongoing)
- Graduate Research Assistant, The University of Arizona, (2013-2019)
- Process Control Engineer Intern, Freeport-McMoRan, (2017-2017)
- Instructor Universidad del Bio-Bio, (2007-2013)
- Electric Engineer, Engineering Consulting JR, (2010-2011)
Interests
Teaching
Electric circuits, artificial intelligence and machine learning, computer vision, control systems, mine automation, robotics and autonomous systems, STEM education
Research
Cyber-physical systems, automation and autonomy, artificial intelligence and machine learning, energy efficiency, energy innovation, robotics, advanced process control systems, optimal control, computer vision, mine automation, fleet management
Courses
Intro to Mine Power Systems
MNE 204 (Spring 2024)
MNE 204 (Spring 2023)
MNE 204 (Spring 2022)
Machine Learning
MNE 559 (Fall 2024)
MNE 559 (Fall 2023)
Independent Study
MNE 599 (Fall 2024)
MNE 599 (Fall 2023)
MNE 699 (Spring 2023)
Research
MNE 900 (Fall 2024)
Thesis
MNE 910 (Fall 2024)
MNE 910 (Summer I 2024)
MNE 910 (Spring 2024)
MNE 910 (Fall 2023)
Licensure & Certification
- Life-Cycle Management of Tailings Facilities, The Tailings Center at Colorado School of Mines, 2025
- Executive Space Course, International Space University, Oslo, Norway, 2024
- Teaching Computation with Matlab and GenAI, Mathworks & Carleton College 2024
- International Engineering Educator, International Society of Engineering Pedagogy (IGIP), 2021
- Energy Innovation and Emerging Technologies, Stanford University, 2020
Select
Journals/Publications
- M. Saavedra, N. Risso, M. Momayez, R. Nunes, V. Tenorio, J. Zhang. Blending Characterization for Effective Management in Mining Operations. Minerals 15 (9), 891Proceedings, 2025.
- E. Wellman, D. Riley, A. Hughes, N. Risso, M. Momayez, J. Kemeny. A Proposed Concept for Classifying Uniaxial Compressive Strength (Ucs) from Swir Hyperspectral Data. Engineering Geology 356, September 2025.
- P. Lopez, N. Risso, A. Anani, M. Momayez. Geohazard Identification in Underground Mines: A Mobile App. Sensors 24 (24), 8052, 2024.
- A. Anani, S.O. Adewuyi, N. Risso, W. Nyaaba. Advancements in machine learning techniques for coal and gas outburst prediction in underground mines. International Journal of Coal Geology 285, 104471, 2024.
- P. Lopez, I. Reyes, N. Risso, M. Momayez, J Zhang. Machine Learning Algorithms for Semi-Autogenous Grinding Mill Operational Regions’ Identification. Minerals 13 (11), 1360, 2023.
- N. Risso, B. Altin, R.G. Sanfelice, J. Sprinkle. Set-valued model predictive control. Computation-Aware Algorithmic Design for Cyber-Physical Systems, 187-207, 2023.
- C. Olmos-de-Aguilera, P.G. Campos, N. Risso. Error reduction in long-term mine planning estimates using deep learning models. Expert Systems with Applications, 119487, 2023.
Proceedings Publications
- J. He, N. Risso, T. Bettencourt, A. Anani. Mining the Text: Automating Safety Insights from Mining Accident Reports. Proceedings of the 10th International Conference on Machine Learning Technologies (ICMLT), 341-349, 2025.
- N. Risso, M. Saavedra, J. Zhang. Mine2Twin: A Synergistic Industry-Academia Collaboration to Improve Engineering Skills for Industry 5.0. 2025 IEEE Global Engineering Education Conference (EDUCON), 2025.
- T. Chimbwanda, A. Anani, N. Risso. Discrete Events Simulation Approach to Investigating the Impact of Electrifying Haul Trucks: A Case Study. Proceedings of the International Conference on Application of Computers & Operations Research in the Minerals Industry (APCOM), 2025.
- N. Risso, L. Cheng, J. He. A Computer Vision-based Platform for Automatic PPE Detection in Underground Environments. Proceedings of the 9th International Conference on Machine Learning Technologies (ICMLT), 2024.
- C. Olmos, N. Risso, A. Anani. Camera-aided Technology for Underground Mine Safety (CAT-UMS), Proceedings of the International Conference on Application of Computers & Operations Research in the Minerals Industry (APCOM), 2023.
- A. Palma, A. Reyes, J. Rohten, N. Risso, D. Quezada, V. Esparza. MPC-based Traction Control for Electric Vehicles. 2022 IEEE International Conference on Automation/XXV Congress of the Chilean Association of Automatic Control (ICA-ACCA).